# Name: Sarjak Maniar
# Email: sm2732@scarletmail.rutgers.edu
# MVA Homework 2
library(readr)
library(ggridges)
library(GGally)
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library(ggvis)
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library(ggthemes)
library(cowplot)
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library(gapminder)
library(gganimate)
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## Consider installing:
## - the `gifski` package for gif output
## - the `av` package for video output
## and restarting the R session
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library(dplyr)
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library(tidyverse)
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library(grid)
library(gridExtra)
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## combine
library(RColorBrewer)
# Bumpus Sparrow Dataset
Bumpus_sparrows <- read_csv("/Users/sarju/Desktop/MITA Sem 2/MVA/Classwork/Week2/Bumpus_sparrows.csv")
## Rows: 49 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Survivorship
## dbl (5): Total_length, Alar_extent, L_beak_head, L_humerous, L_keel_sternum
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Bumpus_sparrows
## # A tibble: 49 × 6
## Survivorship Total_length Alar_extent L_beak_head L_humerous L_keel_sternum
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 S 156 245 31.6 18.5 20.5
## 2 S 154 240 30.4 17.9 19.6
## 3 S 153 240 31 18.4 20.6
## 4 S 153 236 30.9 17.7 20.2
## 5 S 155 243 31.5 18.6 20.3
## 6 S 163 247 32 19 20.9
## 7 S 157 238 30.9 18.4 20.2
## 8 S 155 239 32.8 18.6 21.2
## 9 S 164 248 32.7 19.1 21.1
## 10 S 158 238 31 18.8 22
## # … with 39 more rows
sparrows <- Bumpus_sparrows
sparrows
## # A tibble: 49 × 6
## Survivorship Total_length Alar_extent L_beak_head L_humerous L_keel_sternum
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 S 156 245 31.6 18.5 20.5
## 2 S 154 240 30.4 17.9 19.6
## 3 S 153 240 31 18.4 20.6
## 4 S 153 236 30.9 17.7 20.2
## 5 S 155 243 31.5 18.6 20.3
## 6 S 163 247 32 19 20.9
## 7 S 157 238 30.9 18.4 20.2
## 8 S 155 239 32.8 18.6 21.2
## 9 S 164 248 32.7 19.1 21.1
## 10 S 158 238 31 18.8 22
## # … with 39 more rows
dim(sparrows) # 49 6 [There are 49 rows and 6 columns]
## [1] 49 6
colnames(sparrows) # printing all the column names
## [1] "Survivorship" "Total_length" "Alar_extent" "L_beak_head"
## [5] "L_humerous" "L_keel_sternum"
head(sparrows)
## # A tibble: 6 × 6
## Survivorship Total_length Alar_extent L_beak_head L_humerous L_keel_sternum
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 S 156 245 31.6 18.5 20.5
## 2 S 154 240 30.4 17.9 19.6
## 3 S 153 240 31 18.4 20.6
## 4 S 153 236 30.9 17.7 20.2
## 5 S 155 243 31.5 18.6 20.3
## 6 S 163 247 32 19 20.9
summary(sparrows)
## Survivorship Total_length Alar_extent L_beak_head
## Length:49 Min. :152 Min. :230.0 Min. :30.10
## Class :character 1st Qu.:155 1st Qu.:238.0 1st Qu.:30.90
## Mode :character Median :158 Median :242.0 Median :31.50
## Mean :158 Mean :241.3 Mean :31.46
## 3rd Qu.:161 3rd Qu.:245.0 3rd Qu.:32.00
## Max. :165 Max. :252.0 Max. :33.40
## L_humerous L_keel_sternum
## Min. :17.20 Min. :18.60
## 1st Qu.:18.10 1st Qu.:20.20
## Median :18.50 Median :20.70
## Mean :18.47 Mean :20.83
## 3rd Qu.:18.80 3rd Qu.:21.50
## Max. :19.80 Max. :23.10
str(sparrows) # stars function
## spc_tbl_ [49 × 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Survivorship : chr [1:49] "S" "S" "S" "S" ...
## $ Total_length : num [1:49] 156 154 153 153 155 163 157 155 164 158 ...
## $ Alar_extent : num [1:49] 245 240 240 236 243 247 238 239 248 238 ...
## $ L_beak_head : num [1:49] 31.6 30.4 31 30.9 31.5 32 30.9 32.8 32.7 31 ...
## $ L_humerous : num [1:49] 18.5 17.9 18.4 17.7 18.6 19 18.4 18.6 19.1 18.8 ...
## $ L_keel_sternum: num [1:49] 20.5 19.6 20.6 20.2 20.3 20.9 20.2 21.2 21.1 22 ...
## - attr(*, "spec")=
## .. cols(
## .. Survivorship = col_character(),
## .. Total_length = col_double(),
## .. Alar_extent = col_double(),
## .. L_beak_head = col_double(),
## .. L_humerous = col_double(),
## .. L_keel_sternum = col_double()
## .. )
## - attr(*, "problems")=<externalptr>
boxplot(sparrows[,2:6])
stars(sparrows,labels = sparrows$Survivorship)
# Dive Deeper into Visualization Techniques
attach(sparrows)
#Plots
sparr.birds <- data.frame(as.numeric(rownames(sparrows)),sparrows[,2:6])
sparr.birds
## as.numeric.rownames.sparrows.. Total_length Alar_extent L_beak_head
## 1 1 156 245 31.6
## 2 2 154 240 30.4
## 3 3 153 240 31.0
## 4 4 153 236 30.9
## 5 5 155 243 31.5
## 6 6 163 247 32.0
## 7 7 157 238 30.9
## 8 8 155 239 32.8
## 9 9 164 248 32.7
## 10 10 158 238 31.0
## 11 11 158 240 31.3
## 12 12 160 244 31.1
## 13 13 161 246 32.3
## 14 14 157 245 32.0
## 15 15 157 235 31.5
## 16 16 156 237 30.9
## 17 17 158 244 31.4
## 18 18 153 238 30.5
## 19 19 155 236 30.3
## 20 20 163 246 32.5
## 21 21 159 236 31.5
## 22 22 155 240 31.4
## 23 23 156 240 31.5
## 24 24 160 242 32.6
## 25 25 152 232 30.3
## 26 26 160 250 31.7
## 27 27 155 237 31.0
## 28 28 157 245 32.2
## 29 29 165 245 33.1
## 30 30 153 231 30.1
## 31 31 162 239 30.3
## 32 32 162 243 31.6
## 33 33 159 245 31.8
## 34 34 159 247 30.9
## 35 35 155 243 30.9
## 36 36 162 252 31.9
## 37 37 152 230 30.4
## 38 38 159 242 30.8
## 39 39 155 238 31.2
## 40 40 163 249 33.4
## 41 41 163 242 31.0
## 42 42 156 237 31.7
## 43 43 159 238 31.5
## 44 44 161 245 32.1
## 45 45 155 235 30.7
## 46 46 162 247 31.9
## 47 47 153 237 30.6
## 48 48 162 245 32.5
## 49 49 164 248 32.3
## L_humerous L_keel_sternum
## 1 18.5 20.5
## 2 17.9 19.6
## 3 18.4 20.6
## 4 17.7 20.2
## 5 18.6 20.3
## 6 19.0 20.9
## 7 18.4 20.2
## 8 18.6 21.2
## 9 19.1 21.1
## 10 18.8 22.0
## 11 18.6 22.0
## 12 18.6 20.5
## 13 19.3 21.8
## 14 19.1 20.0
## 15 18.1 19.8
## 16 18.0 20.3
## 17 18.5 21.6
## 18 18.2 20.9
## 19 18.5 20.1
## 20 18.6 21.9
## 21 18.0 21.5
## 22 18.0 20.7
## 23 18.2 20.6
## 24 18.8 21.7
## 25 17.2 19.8
## 26 18.8 22.5
## 27 18.5 20.0
## 28 19.5 21.4
## 29 19.8 22.7
## 30 17.3 19.8
## 31 18.0 23.1
## 32 18.8 21.3
## 33 18.5 21.7
## 34 18.1 19.0
## 35 18.5 21.3
## 36 19.1 22.2
## 37 17.3 18.6
## 38 18.2 20.5
## 39 17.9 19.3
## 40 19.5 22.8
## 41 18.1 20.7
## 42 18.2 20.3
## 43 18.4 20.3
## 44 19.1 20.8
## 45 17.7 19.6
## 46 19.1 20.4
## 47 18.6 20.4
## 48 18.5 21.1
## 49 18.8 20.9
labs.diagonal <- c("Bird","Total length","Alar extent","L. beak & head","L. humerous","L. keel & sternum")
plot(Total_length, Alar_extent,xlab="Total Length (mm)",ylab="Alar extent (nm)",pch=c(16,1))
#Get the Correlations between the measurements
cor(sparrows[-1])
## Total_length Alar_extent L_beak_head L_humerous L_keel_sternum
## Total_length 1.0000000 0.7349642 0.6618119 0.6452841 0.6051247
## Alar_extent 0.7349642 1.0000000 0.6737411 0.7685087 0.5290138
## L_beak_head 0.6618119 0.6737411 1.0000000 0.7631899 0.5262701
## L_humerous 0.6452841 0.7685087 0.7631899 1.0000000 0.6066493
## L_keel_sternum 0.6051247 0.5290138 0.5262701 0.6066493 1.0000000
# Correlations
pairs(sparrows[,2:6])
# Scatter Plot Matrix
# A scatterplot matrix is a matrix of scatterplots that lets you understand the
# pairwise relationship between different variables in a dataset.
#create scatterplot matrix (pch=20 means to use a solid circle for points)
plot(sparrows, pch=20, cex=1.5, col='steelblue')
ggpairs(sparrows)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Let's do a 3D Scatter Plot
library(scatterplot3d)
s3d <- scatterplot3d(Alar_extent,Total_length,L_beak_head,pch=c(1,16)[as.numeric(Survivorship)],xlab="Alar extent", ylab="", angle=45,zlab="Length of beak and head", lty.hide=2,type="h",y.margin.add=0.1,font.axis=2,font.lab=2)
## Warning in scatterplot3d(Alar_extent, Total_length, L_beak_head, pch = c(1, :
## NAs introduced by coercion
mtext("Total length",side=1, adj=1, line=-4.5, font=2)
#Legend added correct symbol for legend point
legend(s3d$xyz.convert(238, 160, 34.1),c("Non-survivor","Survivor"),pch=c(1,16),text.font=2)
# How about factors? Lattice package helps with this.
library(lattice)
super.sym <- trellis.par.get("superpose.symbol")
super.sym$superpose.symbol$pch <- c(1,16,rep(1,5))
super.sym$superpose.symbol$col <- rep("#000000",7)
trellis.par.set(super.sym)
splom(~sparr.birds, groups = Survivorship, data = sparr.birds, ps=0.5, varname.cex = .5,panel = panel.superpose,key = list(columns = 2,points = list(pch = super.sym$pch[1:2], col=super.sym$col[1:2]),text = list(c("Non-survivor", "Survivor"))))
# GGally package
library(GGally)
ggscatmat(sparrows, columns=2:6, color="Survivorship")
## Warning: The dot-dot notation (`..scaled..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(scaled)` instead.
## ℹ The deprecated feature was likely used in the GGally package.
## Please report the issue at <]8;;https://github.com/ggobi/ggally/issueshttps://github.com/ggobi/ggally/issues]8;;>.
stars(sparr.birds)
stars(sparr.birds,labels=row.names(sparr.birds))
# Computing Correlation Matrix
corrm.sparrows <- cor(sparrows[-1])
corrm.sparrows
## Total_length Alar_extent L_beak_head L_humerous L_keel_sternum
## Total_length 1.0000000 0.7349642 0.6618119 0.6452841 0.6051247
## Alar_extent 0.7349642 1.0000000 0.6737411 0.7685087 0.5290138
## L_beak_head 0.6618119 0.6737411 1.0000000 0.7631899 0.5262701
## L_humerous 0.6452841 0.7685087 0.7631899 1.0000000 0.6066493
## L_keel_sternum 0.6051247 0.5290138 0.5262701 0.6066493 1.0000000
plot(corrm.sparrows)
# xyplot
plot(Total_length~Alar_extent)
abline(lm(Total_length~Alar_extent), col="red")
xyplot(Total_length~Alar_extent)
xyplot(Total_length~Alar_extent | Survivorship)
xyplot(Total_length~Alar_extent, groups=Survivorship)
xyplot(Total_length~Alar_extent | Survivorship + L_beak_head)
xyplot(Total_length~Alar_extent | Survivorship , groups=Survivorship, auto.key=list(space="right"))
library(ggplot2)
# Basic scatter plot
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) + geom_point()
# Change the point size, and shape
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) + geom_point(size=2, shape=23)
ggplot(sparrows, aes(x=Total_length,y=Alar_extent)) + facet_wrap(~Survivorship) + geom_point()
# Label points in the scatter plot
# The function geom_text() can be used :
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) +geom_point() + geom_text(label=rownames(sparrows),position = position_dodge(width = 1),vjust = -1, size = 2)
ggplot(data = sparrows, aes(x=Total_length, y=Alar_extent)) +
geom_boxplot(fill=c('steelblue'))
## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
# Add regression lines
# The functions below can be used to add regression lines to a scatter plot :
#
# geom_smooth() and stat_smooth()
# geom_abline()
geom_smooth(method="auto", se=TRUE, fullrange=FALSE, level=0.95)
## geom_smooth: na.rm = FALSE, orientation = NA, se = TRUE
## stat_smooth: na.rm = FALSE, orientation = NA, se = TRUE, fullrange = FALSE, level = 0.95, method = auto
## position_identity
# Add the regression line
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) +
geom_point()+
geom_smooth(method=lm)
## `geom_smooth()` using formula = 'y ~ x'
# Remove the confidence interval
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) +
geom_point()+
geom_smooth(method=lm, se=FALSE)
## `geom_smooth()` using formula = 'y ~ x'
# Loess method
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) +
geom_point()+
geom_smooth()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Change the appearance of points and lines
# We are describing how to change :
# 1) the color and the shape of points
# 2) the line type and color of the regression line
# 3) the fill color of the confidence interval
# Change the point colors and shapes
# Change the line type and color
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) +
geom_point(shape=18, color="blue")+
geom_smooth(method=lm, se=FALSE, linetype="dashed",
color="darkred")
## `geom_smooth()` using formula = 'y ~ x'
# Change the confidence interval fill color
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) +
geom_point(shape=18, color="blue")+
geom_smooth(method=lm, linetype="dashed",
color="darkred", fill="blue")
## `geom_smooth()` using formula = 'y ~ x'
# Scatter plots with multiple groups
# We try to describe how to change point colors and shapes automatically and manually.
# Change the point color/shape/size automatically
# Change point shapes by the levels of Survivorship
ggplot(sparrows, aes(x=Total_length, y=Alar_extent, shape=Survivorship)) +
geom_point()
# Change point shapes and colors
ggplot(sparrows, aes(x=Total_length, y=Alar_extent, shape=Survivorship, color=Survivorship)) +
geom_point()
# Add regression lines
# Regression lines can be added as follow :
# Add regression lines
ggplot(sparrows, aes(x=Total_length, y=Alar_extent, color=Survivorship, shape=Survivorship)) +
geom_point() +
geom_smooth(method=lm)
## `geom_smooth()` using formula = 'y ~ x'
# Remove confidence intervals
# Extend the regression lines
ggplot(sparrows, aes(x=Total_length, y=Alar_extent, color=Survivorship, shape=Survivorship)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)
## `geom_smooth()` using formula = 'y ~ x'
# The fill color of confidence bands can be changed as follow :
ggplot(sparrows, aes(x=Total_length, y=Alar_extent, color=Survivorship, shape=Survivorship)) +
geom_point() +
geom_smooth(method=lm, aes(fill=Survivorship))
## `geom_smooth()` using formula = 'y ~ x'
# Change point shapes and colors manually
ggplot(sparrows, aes(x=Total_length, y=Alar_extent, color=Survivorship, shape=Survivorship)) +
geom_point() +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
scale_shape_manual(values=c(3, 16))+
scale_color_manual(values=c('#999999','#E69F00'))+
theme(legend.position="top")
## `geom_smooth()` using formula = 'y ~ x'
# Change the point sizes manually
ggplot(sparrows, aes(x=Total_length, y=Alar_extent, color=Survivorship, shape=Survivorship))+
geom_point(aes(size=Survivorship)) +
geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
scale_shape_manual(values=c(3, 16))+
scale_color_manual(values=c('#999999','#E69F00'))+
scale_size_manual(values=c(2,3,4))+
theme(legend.position="top")
## `geom_smooth()` using formula = 'y ~ x'
# Adding marginal rugs to a scatter plot
# The function geom_rug() can be used :
geom_rug(sides ="bl")
## geom_rug: outside = FALSE, sides = bl, length = 0.03, na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity
# sides : a string that controls which sides of the plot the rugs appear on.
# Allowed value is a string containing any of “trbl”, for top, right, bottom, and left.
# Add marginal rugs
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) +
geom_point() + geom_rug()
# Change colors
ggplot(sparrows, aes(x=Total_length, y=Alar_extent, color=Survivorship)) +
geom_point() + geom_rug()
# Scatter plots with the 2d density estimation
# The functions geom_density_2d() or stat_density_2d() can be used :
# Scatter plot with the 2d density estimation
sp <- ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) +
geom_point()
sp + geom_density_2d()
# Gradient color
sp + stat_density_2d(aes(fill = ..level..), geom="polygon")
# Change the gradient color
sp + stat_density_2d(aes(fill = ..level..), geom="polygon")+
scale_fill_gradient(low="blue", high="red")
# Scatter plots with ellipses
# The function stat_ellipse() can be used as follow:
# One ellipse arround all points
ggplot(sparrows, aes(Total_length, Alar_extent))+
geom_point()+
stat_ellipse()
# Ellipse by groups
p <- ggplot(sparrows, aes(Total_length, Alar_extent, color = Survivorship))+
geom_point()
p + stat_ellipse()
# Change the type of ellipses: possible values are "t", "norm", "euclid"
p + stat_ellipse(type = "norm")
# bar chart
ggplot(sparr.birds, aes(Survivorship)) + geom_bar(position="stack")
# ggplot(sparr.birds, aes(clarity)) + facet_grid(.~cut) + geom_bar(position="dodge")
# histogram
ggplot(sparrows, aes(Alar_extent))+geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(sparrows, aes(L_beak_head))+geom_histogram(aes(fill = after_stat(count)))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(sparrows, aes(L_humerous))+geom_histogram(aes(fill = after_stat(count)))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# violin plot
# A violin plot is a hybrid of a box plot and a kernel density plot, which shows
# peaks in the data. It is used to visualize the distribution of numerical data.
# Unlike a box plot that can only show summary statistics, violin plots depict
# summary statistics and the density of each variable.
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) + geom_violin()
ggplot(sparrows, aes(x=Total_length, y=L_humerous)) + geom_violin()
ggplot(sparrows, aes(x=Total_length, y=L_keel_sternum)) + geom_violin()
# box plot
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) + geom_boxplot()
## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) + geom_boxplot() + coord_flip()
## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
# density plot and ggridges
# A density plot can be seen as an extension of the histogram. As opposed to the histogram, the density plot can smooth out the distribution of values and reduce the noise. It visualizes the distribution of data over a given period, and the peaks show where values are concentrated.
ggplot(sparrows, aes(x=Total_length)) + geom_density()
ggplot(sparrows, aes(x=Total_length, fill=Alar_extent, color=Survivorship)) + geom_density()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
ggplot(sparrows, aes(x=Total_length, fill=L_humerous, color=Survivorship)) + geom_density(alpha=0.3, aes(y=..scaled..))
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
ggplot(sparrows, aes(x=Total_length, y=Survivorship)) + geom_density_ridges()
## Picking joint bandwidth of 1.63
# hexbin
# A hexbin plot is useful to represent the relationship of 2 numerical variables when you have a lot of data points. Without overlapping of the points, the plotting window is split into several hexbins. The color of each hexbin denotes the number of points in it.
ggplot(sparrows, aes(x=Total_length, y=Alar_extent)) + geom_hex()
ggplot(sparrows, aes(x=Total_length, y=L_humerous)) + geom_hex()
# with ggthemes (see also ggsci, ggthemr)
lastplot <- ggplot(sparrows, aes(x=Total_length,y=Alar_extent)) + xlim(0,3) + geom_point(aes(color=Survivorship)) + stat_smooth() +
labs(x="total length", y="alar extent", title="Bumpus Sparrow Data")
lastplot + theme_bw()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 49 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 49 rows containing missing values (`geom_point()`).
lastplot + theme_cowplot()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 49 rows containing non-finite values (`stat_smooth()`).
## Removed 49 rows containing missing values (`geom_point()`).
lastplot + theme_dark()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 49 rows containing non-finite values (`stat_smooth()`).
## Removed 49 rows containing missing values (`geom_point()`).
lastplot + theme_economist()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 49 rows containing non-finite values (`stat_smooth()`).
## Removed 49 rows containing missing values (`geom_point()`).
lastplot + theme_fivethirtyeight()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 49 rows containing non-finite values (`stat_smooth()`).
## Removed 49 rows containing missing values (`geom_point()`).
lastplot + theme_tufte()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 49 rows containing non-finite values (`stat_smooth()`).
## Removed 49 rows containing missing values (`geom_point()`).
lastplot + theme_wsj()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 49 rows containing non-finite values (`stat_smooth()`).
## Removed 49 rows containing missing values (`geom_point()`).
detach(sparrows)